A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

By Thorsten Joachims. 1997

Subfields: Information Retrieval & Search

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Citations in the corpus

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (2016, methodology): “To demonstrate the versatility of our model to work with graphs generated from unstructured data, we applied our technique to the text categorization problem on the 20NEWS dataset which consists of 18,846 (11,314 for training and 7,532 for testing) text documents associated with 20 classes [15].”
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (2018, methodology): “To gauge whether supervision from a more distant task would still be helpful, we also experiment with a classiﬁer trained on the 20-Newsgroup dataset (Joachims, 1996), for topic classiﬁcation (T OPIC P REPEND -1).”
Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017, methodology): “We use the 20 news groups dataset (Joachims, 1996) in a text classiﬁcation task.”
Bayesian group factor analysis with structured sparsity (2016, methodology): “In this application, we used BASS and related methods for multiclass classification in the 20 Newsgroups data (Joachims, 1997).”
Text Categorization with Support Vector Machines: Learning with Many Relevant Features (1999, methodology): “Then a naive Bayes classifier [2] is trained using only those features ranked 1-200, 201-500,5011000, 1001-2000, 2001-4000, 4001-9962.”
Employing EM and Pool-Based Active Learning for Text Classification (1998, methodology): “Note that our assumptions about the generation of text documents are all violated in practice, and yet empirically, naive Bayes does a good job of classifying text documents [Lewis & Ringuette 1994; Craven et al. 1998; Joachims 1997].”
BoosTexter: A Boosting-based System for Text Categorization (2000, methodology): “Further description of Naive-Bayes and probabilistic TF-IDF for text categorization is given in (Mitchell, 1997; Joachims, 1997).”
A Similarity Measure for Text Classification and Clustering (2014, methodology): “In text processing, the bag-of-words model is commonly used [26], [29], [44].”
Determining the semantic orientation of terms through gloss classification (2005, methodology): “The learning algorithms we have tested are the naive Bayesian learner using the multinomial model (NB), support vector machines using linear kernels, and the PrTFIDF probabilistic version of the Rocchio learner [8](16).”
A Simple Relational Classifier (2003, unknown): “, the naive Bayes classifier [5, 15, 19] or C4.”
Analyzing the effectiveness and applicability of co-training (2000, methodology): “We select four newsgroups from the 20 Newsgroups dataset [11].”